1. Technical Field
The invention is related to noise removal from signals, and in particular, to a technique that adaptively evaluates signals contaminated by approximately stationary noise sources, such as electrical line noise, noise from fans, etc., and develops an adaptive model that allows those noise sources to be directly cancelled from the underlying signal rather than filtered from the underlying signal.
2. Related Art
Noise contamination of signals is a very common problem. For example, one category of noise that frequently contaminates speech recordings (or other sensor-derived signals) includes the well known problem of “stationary tone” interference. In general, stationary tones are noise signals that contaminate an underlying signal at one or more particular frequencies or frequency bands. In other words, a time-frequency representation of an approximately stationary contaminating noise signal is generally represented as an approximately horizontal line having an approximately constant amplitude on a time-frequency domain plot of the contaminated signal. Another way to consider stationary interference of a signal is that the spectral changes of the “stationary” interference over time are much slower than those of the underlying signal that is contaminated by the stationary interference.
Stationary tone noise generally originates from a variety of sources such as direct line noise sources or via acoustic or inductive coupling. Various examples of these types of noise sources include power wiring, inadequate shielding or grounding of microphone or sensor cables, placement of the microphones or sensors near power lines or transformers, etc. Stationary tone noise sources also include noise resulting from positioning microphones or other sensors near TVs, monitors, video cameras, etc., where the microphones can capture interference at frame or line frequencies, either acoustically from transformers or electronically from the cables. Other stationary tone noise sources include relatively constant frequency noise such as background noises coming from the acoustical environment, such as fans, computer hard drives, air conditioning, etc.
A simple example of the effects of stationary tone interference in an audio recording of speech is an audible hum resulting from electrical power line noise. These types of noise are sometimes quite loud relative to the underlying speech signal. Such noise generally occurs at the frequency of the power source (i.e., 50/60 Hz or 400 Hz) and also often occurs at one or more harmonics of those frequencies. Unfortunately, such noise often at least partially overlaps some of the speech frequencies in the audio recording.
Conventional techniques for removing stationary tone noise contamination from signals generally focus on the use of a stationary noise suppressor to filter specific frequency ranges from the signal. Various conventional filter types, such as, for example, notch filters, comb filters, low-pass filters, high-pass filters, band-pass filters, etc., are used to eliminate or pass particular frequency bands of the signal in an attempt to eliminate or attenuate the stationary tone noise in the signal.
The use of conventional filters to remove stationary tone noise from the signal is generally successful in that the noise is eliminated. Unfortunately, where the frequency footprint of the contaminating noise at least partially overlaps the wanted content in the signal, the use of conventional filters to remove that contaminating noise will also remove wanted content from the signal. Further, such filtering often introduces unwanted artifacts, such as, for example, nonlinear distortions, “musical” noises, etc., into the filtered signal, resulting in a substantially distorted signal.
Other, more complex, approaches to noise suppression have been developed to suppress stationary tone interference or noise in signals while creating less distortion to the underlying wanted signal content. These more complicated approaches typically operate by closely tracking frequencies of noise in a time-frequency representation of the signal to identify the spectral lines of noise in the signal for use in removing noise content from the signal. Unfortunately, these noise suppression techniques are generally computationally expensive and not typically appropriate for real-time noise cancellation. In fact, many such techniques are used to process audio signals offline rather than in real-time.